Prediction of heart disease using SVM

Abstract

Support Vector Machine (SVM) is an important classification method in data mining. It is a supervised classification technique. It finds a hyperplane for classification of the target classes. The heart disease consists set of disorders affecting the heart. It includes blood vessel problems such as irregular heart beat issues, weak heart muscles, congenital heart defects, cardio vascular disease and coronary artery disease. Coronary heart disorder is a familiar type of heart disease. It reduces the blood flow to the heart leading to a heart attack. In this paper the UCI machine learning repository data set consisting of patients suffering from heart disease is analyzed using support vector machines. The classification accuracy of the patients suffering from heart disease is predicted. Implementation is done using R language.